This application relates to a general purpose method and apparatus which employs a unique knowledge engine, and an associated unique library (and other) structure, to perform focused assessments and diagnoses of various problems and situations. In particular, it discloses such an invention which strongly mimics the natural human thought process, and which is endowed with a powerful interactive and adaptive capability to grow and “learn” in every subject area to which its “attention” is directed. It is usable in all subject areas, or domains, of knowledge.
For the purpose of illustration herein, a preferred embodiment of, and a manner of practicing, the invention are described herein principally in the context and knowledge domain of medical diagnosis.
The present invention marks a significant departure from conventional, so-called artificial intelligence systems and processes, and offers a notable opportunity to fulfill the long-standing desire to link the processing power of a computer to an algorithmic approach which truly patterns (problem-/and situation-assessment) performance closely to the ways in which the human mind actually processes such activity.
With this desire held in mind, conventional artificial intelligence machines and methods have two general limitations. First of all, they are usually based upon linear decision processes. Secondly, they tend to be designed around specific applications, and are especially so designed in such a manner that the particular application per se dictates the architecture of the associated system and methodology. They have a strong singular focus. Linear-decision models, the conventional landscape, involve embedded data, in the sense that the applicable data structure is part of the decision-making architecture itself. This condition limits the possible outcomes of assessment behavior, and requires a significant overhaul of a system and of its associated methodology every time that new data is incorporated therein. Such linear-decision architecture, which essentially is a rule-based architecture, limits flexibility because of the fact that a user must follow certain designed pathways, even if those pathways are not optimal for the particular problem at hand. Domain-specific applications suffer from similar problems, since the underlying architecture therein is restricted by domain-specific data sets.
The system and methodology of the present invention, as will be seen, overcome all of these limitations, and provide a functionally superior, non-rule-based, model of human-mimicking machine intelligence. For example, in accordance with implementation and practice of the present invention, data sets are totally modular. Changes can be made in the applicable knowledge repository without disrupting the fundamental, available assessment processes in any way. This condition allows the system and methodology of this invention easily and readily to expand its fund of knowledge without any of the limitations that have restricted the scalability of previous, expert, artificial intelligence systems.
Basically, rules or knowledge-based systems, artificial intelligence systems, use ‘hard’ Boolean logic architectures. Such systems have utility but are hampered by their linearity and rigid knowledge structures—i.e. they contain data embedded within a process structure. To incorporate new data into such a structure requires a substantial re-write of the corresponding process, or processes. This becomes a large data-maintenance problem as complexity of a knowledge domain increases. Another limitation is that designers of such systems must anticipate all possible relationships within the relevant data set in order to field a reliable system. This can also be a limitation of classic neural network architectures.
Classic fuzzy logic, or Bayesian nodal systems, invariably depend upon statistical analysis. Numerous data propagation and maintenance issues are associated with such systems. There are two main limitations for practical decision support application. One, statistical relationships are not static within subject (or subject areas of interest). And two, statistical relationships themselves break down at the level of the individual. Presentation of statistical information to decision makers may actually complicate decision making. Systems using statistical methods are by definition limited in applicability in early warning situations or where ‘out of box’ thinking (recognition of low probability issues) is required in order to recognize instances where rare situations, conditions or threats may in fact be present.
By way of further contrast with prior art artificial intelligence technology, and in terms of important offered advantages, the system and method of the present invention are not limited to operation in but a single knowledge domain. More specifically, the invented system and method can work universally in any knowledge discipline, can handle a large number of potential assessment results with great ease and stability, and can rapidly and seamlessly perform complex assessments involving thousands of data elements. It will not choke, even on massive, data-intensive issues. As will become apparent, the invention can readily be integrated for use with a wide variety of existing, knowledge-domain-associated relational databases, and within all operating environments, can perform with a remarkably “human ability” to alter the direction being taken during an assessment operation based upon newly encountered data. Additionally, the system and methodology of the present invention offer the further advantages that the system and method: (a) essentially use natural-language text structure to communicate with users, thus making extensive user training unnecessary; (b) can receive and process input data without any concern or requirement for defined-order input; (c) will consider all available data each time that there is a “run” of assessment behavior; (d) can link assessment activities to documented research relating to any selected knowledge domain; and (e) can properly process both vague, minimal assessments, as well as detailed assessments.
The invention is scaleable, and is capable of embracing the full weight of any subject area. Uniquely, it links, as assessment companions and “co-workers”, the worlds of both inference and statistical analysis. It can undertake an assessment task with very modest and sketchy inquiry-input information delivered in any sequence or order. It can refine an assessment task by directing inquiries to, and soliciting related responses from, a user, and can create sophisticated and tightly focused output assessments in a easily understandable natural-language manner (as just mentioned above).
The functional building blocks of the method and apparatus of this invention take the form of elemental and fundamental, inferential components which are referred to herein as elemental data points (EDPs). Two types of such EDPs are employed. One is referred to as a simple EDP, and the other as a complex EDP. A simple EDP consists of a singular data component, such as the word “shoulder” in a medically focused embodiment of the invention. A complex EDP consists of the associated combination of a single problem type, such as the word “pain” (in the medical field), and at least one data component, such as the word “shoulder” just mentioned. As will be more fully explained shortly, FIG. 2 in the drawings, still to be described herein, verbally diagrams the anatomies of these two kinds of EDPs.
These EDPs are lowest-common-denominator-type elements that relate to, and represent, a wide spectrum of characteristics (ultimately all that can be identified) which are relevant to the possibilities, variations and permutations of matters involving particular, selected subject areas, or domains. Put another way, each EDP permits no further relevant subdivision that will, during an assessment process, enhance the capability for further problem and/or situation assessment differentiation. Methodology practiced in accordance with the invention is employed to generate and organize such EDPs, and also to produce another category of elements referred to herein as Result Keys.
A Result Key, according to the invention, is a collection of EDPs that represent a unique presentation of an assessment result that is known and documented, and which is assigned a particular degree of certainty. A Result Key is thus a combination of EDPs that define a reportable result with some reliable degree of certainty. Result Keys are effectively “organized” into identifiable Master Keys, where each Master Key is effectively a collection of all EDP's that are associated with a single result, and Result Keys are identifiable collections of these EDPs which point, with different degrees of certainty, to that same result.
Another important element of a defined knowledge domain is referred to herein as a “problem type” (mentioned briefly above). As was stated earlier, so-called complex EDPs are made up of one or more data components grouped in the context of a problem type. A problem type is a distinct category of information, organized hierarchically for classifying a problem for a knowledge domain in a manner that mimics the way experts in that knowledge domain think of problems and situations. Ideally, the universe of problem types will be inclusive of all known problems within a particular knowledge domain. Problem types offer a convenient and effective entry point for users of the system and methodology of this invention for describing the problems and situations that they are wishing to have assessed.
TABLE I below diagrams the relationships of EDPs, problem types, and data components:
TABLE IProblem TypeData ComponentSimple EDPn/aPatient Age Band: 30-49Complex EDPPainLocation: ShoulderOnset: SuddenFrequency: Constant
Associated with each EDP, in accordance with the invention, are two usage indicators which indicate whether the EDP (a) can be directly employed as part of a Result Key, and/or (b) whether the EDP can be used as part of a reported assessment. TABLE II immediately below generally shows how such indicators can exist:
TABLE IITypeClassifications whereYYThis would be considered a “normal”the associated datacomplex component.components must beprovided in the contextof a problem type.YNThis situation would be used topreserve a normalized view ofcomplex components in order for thecomponents to support shortcuts.NYThis represents complex datacomponents that can be added to anassessment for documentation only,but are not considered by the adaptiveknowledge engine.NNThis is not a valid combination.Classifications where aYYThis would be considered a “normal”data component issimple component.complete without beingdefined in the context ofa problem typeYNThis would be used to preserve anormalized view of simple compo-nents in order for the components tosupport shortcuts.NYThis would be used for simple com-ponents that can be added to anassessment for documentation only,but are not considered by the adaptiveknowledge engine.NNThis is not a valid combination.Classification thatYYThis is generally not a valid combina-represent syndromes,tion because these highly granularwhich are single datacomponents are only accessible viacomponents thatthe refinement process of the adaptiverepresent a highlyknowledge engine.granular complex set ofcharacteristics.YNThis is the typical scenario forsyndromes and other special simplecomponents. This allows keys to bebuilt for them yet their inclusion in anassessment is done outside the initialdata capture process, with processessuch as refinement, default compo-nents, etc.NYThis is not a common scenario, butcould be used to capture highlygranular data for documentationpurposes only, without being consid-ered by the adaptive knowledgeengine.NNThis is not a valid combination.
The creation and use of such EDPs and Result Keys enables a still further important feature of the invention which is that, during an assessment, the system and methodology of this invention can approach the task of arriving at a reportable result by noticing the absence of some quality or characteristic that relates (a) to the original input inquiry data, and/or (b) to responses which are received from a user during what is more fully described below as an assessment refinement process. For example, in the field of medicine, a field wherein the invention has been found to offer particular utility, and which is employed herein as a model to illustrate the invention, the absence of some particular characteristic of good health can indicate the impending emergence of some infirmity. As a consequence, the invention offers an impressive opportunity, in this field, to give very early warnings about the onsets of potential medical problems.
In another field, such as, for example, the field of materials processing, the method and apparatus of the invention might notice the absence of a stream of certain processing-related data, which absence might indicate the occurrence of a failed processing step.
Importantly, the inferential database employed according to the invention is independent of the algorithm(s) employed by the knowledge engine during an assessment. This independence strongly supports the open versatility with which the structure and methodology of the invention perform.
Three of many powerful aspects of the system and methodology of this invention are: (a) that inferential, elemental data components are constructed to possess the characteristics and qualities mentioned above; (b) that a practice referred to herein as relevance short-cutting (shortly to be described) fuels remarkable efficiency in the assessment processing which is performed by the knowledge engine that is part of the system; and (c) that the practice of such short-cutting enables “lateral” investigations which cut across and embrace plural problem types, and even plural problem types that reside in plural, analogous knowledge domains. This unique “lateral” capability especially models human cognitive thinking, and avoids the linear decision-making trap which confines the capabilities of conventional artificial intelligence systems and methods.
The process and practice of so-called short-cutting relates to how data components are handled according to the invention. A short-cut data component, also referred to herein as a normalized data component, is a single data component which is associated with one problem type, and which acts as a surrogate for relevant, plural, other data components (non-normalized data components) that are associated with the same problem type, and/or with another, or plural other, problem type(s). Assessment relevance is the principal context within which short-cuts are created. As will be seen, relevance short-cuts by creating and organizing related bodies of normalized and non-normalized data components. significantly enhance the performance of the structure and methodology of this invention.
A simple illustration given immediately herebelow will illustrate the concept of relevance short-cutting. This illustration is set in the context of an assessment wherein the user is entering information regarding the lateral orientation of a medical phenomenon/issue. EDP entry value choices include: Left Side; Right Side; Both Sides; One Side Only—a total of four EDP possibilities. Relevance short-cutting normalization of this nominally four-EDP population causes the “values” of “Left Side” and “Right Side” to be representable also as “One Side Only”. Hence, the two values “Left Side” and “Right Side”, which exists as definitive, plural individuals from a non-normalized point of view, are treated as the single, integrated value “One Side Only” from the normalized point of view. The importance of this multiple-to-singular short-cutting practice will be more fully discussed later herein.
A further important contribution of the present invention is that it employs statistical analysis, utilizing past system performances to enhance the confidence levels of results produced in subsequent (downstream) assessments. During assessment activity, the system and method of this invention implement refinement sub-processes which thoughtfully elicit additional guided input information to help close in on the best obtainable assessment result. Data obtained during assessment performances are collected and stored in a manner whereby the knowledge engine in the system can perform statistical analysis to grow and improve the quality and effectiveness of the resident, underlying, inferential database which fuels system behavior.
These and many other features and advantages that are offered by the present invention will become now more fully apparent as the detailed description which follows is read in conjunction with the accompanying drawings.